Prioritized and predictive intelligence of things enabled waste management model in smart and sustainable environment

Collaborative modelling of the Internet of Things (IoT) with Artificial Intelligence (AI) has merged into the Intelligence of Things concept. This recent trend enables sensors to track required parameters and store accumulated data in cloud storage, which can be further utilized by AI based predictive models for automatic decision making. In a smart and sustainable environment, effective waste management is a concern. Poor regulation of waste in surrounding areas leads to rapid spread of contagious disease risks. Traditional waste object management requires more working staff, increases effort, consumes time and is relatively ineffective. In this research, an Intelligence of Things Enabled Smart Waste Management (IoT-SWM) model with predictive capabilities is developed. Here, local sinks (LS) are deployed in specified locations. At every instant, the current status of smart bins in each LS is notified to users to determine the priority level of LS to be emptied. Based on aggregated sensor values for the three smart bins, LS weight and poison gas value, the priority order of emptying LS is computed, and decision is made whether to notify the users with an alert message or not. It also helps in predicting the LS, which is likely to be filled up at a faster rate based on assigned timestamp. This model is implemented in real time with many LS and it was observed that bins, which were close to more crowded sites filled up faster compared to sparse populated areas. Random forest algorithm was used to predict whether an alert notification is to be sent or not. An average mean of 95.8% accuracy was noted while using 60 decision trees in random forest algorithm. The average mean execution latency recorded for training and testing sets is 13.06 sec and 14.39 sec respectively. Observed accuracy rate, precision, recall and f1-score parameters were 95.8%, 96.5%, 98.5% and 97.2% respectively. Model buildup and the validation time computed were 3.26 sec and 4.25 sec respectively. It is also noted that at a threshold value of 0.93 in LS level, the maximum accuracy rate reached was 95.8%. Thus, based on the prediction of random forest approach, a decision to notify the users is taken. Obtained outcome indicates that the waste level can be efficiently determined, and the overflow of dustbins can be easily checked in time

➢ Authors have divided the section 3 into 2 sub-sections 3.1 and 3.2 in the revised manuscript according to the content.
❖ The results are well presented but not so nicely discussed in Section 4. I recommend the authors to elaborate better on the discussion.
➢ The results analysis section 4 is revised and more details about the results are included in the revised manuscript.
❖ I suggest the authors divide the text in the conclusion in at least 3 paragraphs. Two for the conclusion, and a third, and last one, for discussions on future works.
➢ Authors have segregated the conclusion part into 3 separate paragraphs. Future scope forms the last paragraph of conclusion.

Reviewer 2#
❖ Numerous solutions to this problem have been proposed in past research.

Please explain novelty and contribution of the study. Why this study is important and what academic values it adds to the field?
➢ Though many works have been done in context to this domain, yet our research retains novelty and offers numerous benefits. Majority of existing models are not sustainable enough, incurs considerable delays and is not suitable for large scale applications. They fail to handle different heterogeneous wastes. Also, many models are mostly utilized for only data collection purpose, but decision-making ability is absent in those models. Apart from these concerns, majority of existing solutions either deploys an IoT based module or a simple machine learning based application. A hybrid integrated combination of both sensory based IoT nodes and predictive capabilities are rarely implemented in large scale. In comparison of these existing models, our work offers substantive benefits than others in terms of accuracy, scalability, reliability, robustness with least delays. It is more sustainable and can be applied for more diverse locality with accumulation of heterogeneous garbage. Sensory based data collection and storage can be done in an uninterrupted manner. Also, the collected data can be stored in cloud and can be used for taking decision on number of bins used and identifying denser locality with huge waste accumulation. Random forest is used for classification which makes it more accurate, time saving and free from bias. Also, presence of any discrepancies related to smart bins functioning or presence of poisonous substance can be detected and notified to society.
The important benefit of the model is highlighted in section 4.3.

❖ The introduction needs to be rewritten more precise and concrete and providing the much better motivation, significance and impact of the paper. Please explain research gap explicitly then propose research questions.
➢ The introduction section is completely restructured and made more compact and precise. Only relevant information is retained. The motivation, research gaps are included with clarity and later the contribution of the research work is written. ❖ The discussion of Experiment should be written more in-depth, more precise and concrete, such as what questions were resolved? How can the proposed method solve these problems? Then, the advantages (and disadvantages?) of the proposed methods should be discussed.
➢ The result analysis is discussed in more detail in the revised manuscript. The benefits of the proposed model are outlined in section 4.3. The solutions offered in this paper can help to resolve research gaps highlighted in the paper.
❖ In the conclusion, the practical application field of the proposed methods and the research findings can be described that highlights the contribution of this article.
➢ The conclusion is divided into three paragraphs highlighting the main contribution of the research, methodology used in research and the future scope of the research.
Thanks again for your invaluable comments which significantly improved the paper.
Tarek Gaber The corresponding author